Digital reputation management

ABSTRACT

Embodiments described herein disclose methods and systems for managing a digital reputation of a user. The exemplary method can receive information about an item to be purchased by the user, determine in real-time or near real-time, based on one or more financial factors, a first digital reputation score indicative of an effect of the item on the user&#39;s digital reputation, determine an alternative item to be purchased instead of the item to be purchased, and determine a second digital reputation score based on the purchase of the alternative item.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a non-provisional of and claims priority to U.S.Provisional Application No. 62/626,545, filed on Feb. 5, 2018, entitled“DIGITAL REPUTATION MANAGEMENT SYSTEMS AND METHODS,” which is herebyincorporated by reference in its entirety for all purposes.

BACKGROUND

In today's data driven world, individuals provide personal informationabout themselves with every digital interaction without ever realizingthe consequences of providing personal information. Companies ororganizations collect and analyze personal information obtained fromindividuals' digital interactions to form a comprehensive digitalpersonal reputation about each of those individuals. A digital personalreputation may not only impact an individual's online, digital, orvirtual presence but it may also impact that individual's real-lifeinteractions. For example, if a consumer uses a credit card for afinancial transaction, the consumer may not only hand over personalinformation in that transaction but may also hand over a way to trackthat consumer's behaviors. Attempts to prevent online, digital, orvirtual presence by only avoiding credit card transactions may not besuccessful. For example, in a transaction involving only cash, aretailer may still be able to track purchases through rewards programs.The divulging of personal information may not be optional or transparentto the consumer. This personal information may be used by marketers,retailers, or financial institutions in a manner unapproved by theindividuals. Individuals do not have a say in who has access to theirpersonal information, how their personal information was obtained, andhow their personal information will be used. Thus, individuals are notable to properly identify and control their personal informationcollected by companies or organizations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A shows an example of a digital data reputation score report.

FIGS. 1B-1D show the reputation sub-scores associated with variousfacets that can be selected by a user.

FIGS. 2A-2C show embodiments of a reputation shopper application.

FIGS. 3A-3B show embodiments of a reputation data mapper application.

FIG. 4 is a block diagram illustrating an overview of devices on whichsome implementations can operate.

FIG. 5 is a block diagram illustrating an overview of an environment inwhich some implementations can operate.

FIG. 6 is a block diagram illustrating components which, in someimplementations, can be used in a system employing the disclosedtechnology.

FIG. 7 is a flow diagram illustrating a process used in someimplementations for managing a digital reputation of a user.

The techniques introduced here may be better understood by referring tothe following Detailed Description in conjunction with the accompanyingdrawings, in which like reference numerals indicate identical orfunctionally similar elements. Moreover, while the technology isamenable to various modifications and alternative forms, specificembodiments have been shown by way of example in the drawings and aredescribed in detail below. The intention, however, is not to limit thetechnology to the particular embodiments described. On the contrary, thetechnology is intended to cover all modifications, equivalents, andalternatives falling within the scope of the technology as defined bythe appended claims.

DETAILED DESCRIPTION

An individual's digital reputation can be used to determine variousaspects of that individual's life. For example, an individual'sfinancial abilities such as credit worthiness can be determined not onlyfrom his or her payment history, but it can also be determined by thatindividual's behavior data. A person may be deemed capable ortrustworthy based not only on face-to-face interactions, but also by hisor her digital interactions. For instance, while an individual'sprofessional credibility can be based on traditional publications orspeaking engagements at conferences, that individual's professionalcredibility can also be determined by his or her global reach or thenumber of followers following that individual on social media platformssuch as on LinkedIn.

The embodiments disclosed in this document can be used by individuals tocontrol or improve their digital reputation. For example, the variousembodiments can allow individuals to determine who has access to theirpersonal information, how such personal information was obtained, howthe personal information will be used, or how to improve their digitalreputation. As further described in the sections below, in someembodiments, a digital data reputation platform may include any one ormore of a digital data reputation score report, a reputation shopperapplication, a reputation data mapper application, and a reputation datamasker application.

Digital Data Reputation Score Report

FIG. 1A shows an embodiment of a digital data reputation score report100 that can provide an individual with his or her reputation relatedinformation. As further described in the sections below, the digitaldata reputation platform can generate the digital data reputation scorereport 100. The digital data reputation platform can provide anindividual with the tools needed to determine what personal data isbeing mined from the individual, how that personal data is being used,or how the individual provided that personal data. The individual canuse this information to determine whether he or she should alterbehavior to either stop providing the personal data or alter the data toimprove his or her reputation in a desired way.

In some implementations, the digital data reputation score report 100can include a data driven score comprised of one or more facets aboutthe individual. For example, as shown on the right hand side of FIG. 1A,the digital data reputation score report 100 can include a personalfacet 108, a professional facet 112, and a financial facet 116. Thepersonal, professional, and financial facets can describe anindividual's personal reputation, professional reputation, and financialreputation, respectively. In some implementations, a digital reputationscore for each facet can be indicated with marker 110 on a line or bar109. In some implementations, the line or bar 109 may include colorsthat can indicate a range of digital reputation scores that can becharacterized as poor 109 a, moderate 109 b, or excellent 109 c. As anexample, a marker 109 shown in the moderate digital reputation scoresection 109 b indicates that a user's personal reputation is moderate.The professional and financial facets may also include line or bar 113,117 that can be used to characterize a user's reputation for thosefacets. In some implementations, the line or bar 113, 117 may includecolors that can indicate a range of digital reputation scores that canbe characterized as poor 113 a, 117 a, moderate 113 b, 117 b, orexcellent 113 c, 117 c. As shown as an example, the markers 114 and 118indicate that the user's professional reputation is excellent but thathis financial reputation is poor.

Each facet's score can be determined by data received by the digitaldata reputation platform from any one or more of numerous sourcesavailable on the internet, through retailers, solicitors, social media,publications, self-published content, credit card information, onlineshopping data, web history, or any other digital data store that can beobtained either thru digital mining or through the user's consent. Theuser can provide the digital data reputation platform with personalinformation such as home or office address, social media account logins,bank accounts, social security number, or credit report information. Insome implementations, the user's digital reputation score can bedetermined by the digital data reputation platform by scanning internetwebsites for reputational information related to the user, analyzing thereputational information based at least in part on a set of keywords,scoring the reputational information based at least in part on a set ofkeywords specific to each website.

As an example, a user's personal digital reputation score may bedetermined by the number of friends that the user has on social media,the user's social media postings, or any publicly available informationabout the user. In another example, a user's professional digitalreputation score may be determined by the number of connections that theuser has on professional social media websites, the user's speakingengagements, conference attendance, publications, or any other publiclyavailable information. In yet another example, the digital datareputation platform can determine a user's financial digital reputationscore based on one or more financial factors. As an example, the one ormore financial factors may include the user's spending habits (e.g.,brands, method of payment, frequency of purchase, chosen retailer,location of purchase), the user's saving habit, the user's age, theuser's income, the user's shopping subscriptions, the user's socialmedia postings, the user's bankruptcies, or publicly availableinformation. In some embodiments, a user's score can also be affected byother facets. For example, if the user frequently purchases lumber andthey also obtain income from items made and sold from lumber, lumberpurchases might positively affect their financial score. However, if thelumber purchases are for a hobby or project and no known income isassociated with that purchase, the lumber purchases might have no effector a negative effect on the financial score. The score determination canprovide as comprehensive a view of the user as possible using all theavailable data sources in relation to each other and can createcorrelations based on known behavior models such as those that might bederived from machine learning.

In some implementations, the digital data reputation platform candetermine whether a digital reputation score is poor, moderate, orexcellent based on whether the digital reputation score is withincertain predetermined ranges. For example, a digital data reputationplatform may consider digital reputation scores of 0-33 as poor scores,34-66 as moderate scores, and 67-100 as excellent scores. In some otherimplementations, the digital data reputation platform can determinewhether the digital reputation score is above or below a threshold tocharacterize the score as poor, moderate, or excellent.

In some implementations, the user can adjust the weight each facet playson the overall score shown in section 120 based on which facet is moreimportant to the user. The overall digital data reputation score can becomprised of the scores from the facets and what weight each facet hason the overall score can be customized by the user if they chose tofocus on certain facets over others. The overall score section 120 mayinclude line or bar 121 that can be used to characterize a user'soverall digital reputation score. In some implementations, the line orbar 121 may include colors that can indicate a range of digitalreputation scores that can be characterized as poor 121 a, moderate 121b, or excellent 121 c. As shown as an example, based on the weightassigned by the user to each facet, the marker 122 indicates that theuser's overall reputation is moderate

In some implementations, as shown on the left-hand side of FIG. 1A, theuser can be provided with a summary of what activities or personal dataare helping the digital reputation score section 102, hurting thedigital reputation score section 104, and are recommended for the usersection 106 for each facet. The activities recommended for the usersection provides an activity or activities to try to improve his or herdigital reputation score in the various facets. The summary can beprovided for each facet, such as the personal, professional, andfinancial facets as shown in sections 102-106 in FIG. 1A. Sections102-106 can also provide information such as what personal data may becontributing to the score and that data's likely source. In someimplementations, sections 106 can also provide information about how tosecure the data source.

The digital reputation score can help a user see what aspects of theirpersonality dominate and learn what they can do to adjust thatperception, if desired by the user. For example, one user's reputationis deemed by the digital data reputation score report 100 to be aconservative professional with little social life. This can be indicatedby a poor digital reputation score for the personal facet 108 and amoderate to excellent digital reputation score for the professionalfacet 112. However, if such a user would prefer to be viewed as morecreative with a strong presence in the Sci-Fi community then higherscores in different facets may be desirable. The digital data reputationscore report 100 can identify in the recommend for user section 106 theactivities or behaviors that the user may have to do to have a greaterpresence in the Sci-Fi community. As another example, if a user prefersto be viewed as an artist or a humanitarian, the user may use thedigital data reputation platform to select his or her idols such asTaylor Swift or Mother Teresa. The platform can receive such informationand can recommend the activities that the user may want to pursue toimprove his or her digital reputation score to more like his or heridol.

User data for the reputation platform can be gathered and harvested in anumber of ways. The least desirable method is expecting the user toinput this as most often such models will fail due to lack of adoptionand the burden of data entry. As a result, autonomous data gathering ispreferable. Reputation data for personal facet could be obtained bygathering data from intelligent sensors (real and virtual), e.g.,emotion detection, conversational AI and machine learning techniques todetermine mood and behavior of conversation. Personal behavior such ascommunity help, volunteering, and such can be gathered by external datasources that capture them. Financial facet reputation related data canbe obtained from external systems as well as edge devices such as amobile phone, a smart watch, etc. Professional facet data come beobtained from external data sources, community ratings (e.g., LinkedIn),social media, and professional engagements (conferences, speakingengagements, influencers, etc.).

The collected data can become fodder for AI and machine learning tounderstand and benchmark characteristics and threshold ranges for eachcategory to determine when a user is allowed or should be recommended tobe included as part of a community (e.g., Sci-Fi). This data modelcontinuously changes as more people are on boarded and theirmulti-faceted reputation data is harvested over time and new learningsare gathered.

FIG. 1B illustrates that the personal facet score can include sub-scoresto characterize one or more personal attributes of an individual. Asshown in FIG. 1B, some examples of personal attributes may includefamily, friends, community, lifestyle, faith, and health. In someimplementations the various listed personal attributes may be selectedby the user. The personal facet can provide the user with a view ofthemselves that is closest to how others in the user's family and socialcircle characterize that individual. An example of an item listed in thehelping section 150 may be a user's gym membership that may haveincreased the user's personal digital reputation score for friendsbecause the digital data reputation platform determines that there wasan increase in the user's friends on social media after the user joinedthe gym. In the hurting section 152, the user may be informed of abehavior or habit, such as gambling, that may hurt his or her personaldigital reputation score. In the recommended for user section 156, anexample recommendation for how to stop data leaks in the personal facetmight be to alter the user's security setting on social mediaapplications. Another example of a recommendation to alter behaviormight be to stop using Facebook and switch to Twitter because a majorityof the user's personal circle have migrated activity to Twitter. Yetanother example of new behavior to try might be to make a publicdonation to a certain non-profit because the digital data on that userindicates that type of content is likely to improve the user's social orcommunity standing.

FIG. 1C illustrates that the professional facet score can includesub-scores to characterize one or more professional attributes of anindividual. The professional facet score can characterize anindividual's professional life including, for example, the user'spresence in their respective professional community, the prestige anddepth or breadth of the user's network, existence and consumption ofphysical or electronic publications, speaking engagements orconferences, number of professional recommendations, and perceivedprofessional experience. In the recommended for user section 164, anexample of a suggestion on how to reduce data leaks may include to orderpublications using the office address instead of the user's homeaddress. Another example on how to alter behavior might be to alter theuser's online professional profile, like one you might find on LinkedIn.Yet another example of a new behavior the application might recommendwould include attending a new conference or writing a professional blogto build professional credibility.

FIG. 1D illustrates that the financial facet score can includesub-scores to characterize one or more financial attributes of anindividual. The financial facet score characterizes the user's financialattributes as a potential lender, borrower, investor, or overallfinancial stability. The financial facet can tell the user what theirspending, saving, and investing habits say about them. For example, thefinancial facet score and associated sections 170-174 can inform a userwhether he or she is taking on the appropriate level of risk to matchtheir income, age, or other factors. The financial facet can makerecommendations of what behaviors to continue, stop, or try to improveone's financial security. In some implementations, the financial facetmay be used to provide more than basic financial advice and provideinformation such as altering consumer behaviors. For example, a usermay, based on his or her purchases, be recommended to stop shopping atone store and shop at another store instead. As another example, thedigital data reputation platform may recommend a user to reduce thenumber of online shopping subscription. In yet another example, a usermay be recommended to try something new such as to increase spending onone type of credit card and reduce spending on another type of creditcard to improve the rewards received by the user's credit card usage.The digital data reputation platform may also identify financial dataleaks. For example, the system may recommend that the user use Apple payor cash at a store that sells the user's purchase data based on usage ofa rewards account.

In some implementations, the digital data reputation platform (shown as464 in FIG. 4) can be an online or mobile application solution thatanalyzes the digital footprints of a consumer to gather digital dataabout that consumer that can range from the consumer's purchasing andpayment behavior to the consumer's social media and digital publicationbehavior. The digital data reputation score report 100 shown in FIG. 1Amay be provided to the user through a web browser, through a mobileapplication, or in an e-mail message. The digital data about a consumercan include both data that is provide by the consumer as well as datathat is created by other parties about the consumer. The digital datareputation platform analyzes the available data to create a digitalreputation score broken down into one or more facets, such as personal,professional, or financial.

In some embodiments, in addition to assessing the overall health of anindividual's digital data reputation, the digital data reputationplatform can also reveal to the consumer the website or online servicewhere certain personal data is most accessible, how that personal databecame visible, or how that personal data can impacting the overallscore or the score of the one or more facets. An example of the platformdisclosing how personal data because visible may include a consumer'sAmazon purchases that were tracked because the consumer used a creditcard and that transaction data was mined. As discussed above forsections 102-106 of FIG. 1A, the digital data reputation platform canalso make recommendations for how the user can stop or reduce dataleaks, change existing behavior to improve the data's impact on thescore, or try a new behavior to improve the digital data reputationscore.

In some implementations, the digital data reputation platform can runone or more hypotheticals to simulate potential impacts to the scorebased on certain behaviors.

In some embodiments, the digital data reputation platform can usemachine learning to develop an overall picture of the various digitalreputations with indications of what activities put users in whichcategories. Such information can be determined by looking at the digitalinformation for many people (e.g., people in a work environment, city,town state, country) and harvesting the data and using machine learningto refine the activities that place the digital data reputation ofpeople in one category or another. Thus, the user can be providedinformation regarding the general population and where the user'sreputation fits in based on the available digital data.

Reputation Shopper Application

FIGS. 2A-2C show embodiments of a reputation shopper application thatcan be implemented on a mobile application or a web browser. Thereputation shopper application can help consumers in their shoppinghabits to inform and guide them on how their shopping habits may impacttheir reputation. In some implementations, a consumer may use a mobileapplication on a portable electronic device to scan the item for anin-person purchase as shown in FIG. 2A. The consumer can scan the itemfor purchase by scanning the bar code on the item or by taking a pictureor a photo of the item. In some other implementations, the consumer mayuse a web browser or the mobile application to add the item to theironline shopping cart as shown in FIG. 2C.

FIG. 2B illustrates that the reputation shopper application can informthe consumer of the impact or effect, if any, of the item to bepurchased on the consumer's digital reputation score. For example, thereputation shopper application can use the digital data reputation scoreplatform to determine whether the item to be purchased, such as the icecream shown in FIG. 2B, results in a higher, lower, or the samefinancial digital reputation score based on one or more financialfactors as discussed in this document. In some implementations, thereputation shopper application can determine in real-time or nearreal-time the effect of the item to be purchased on the consumer'sdigital reputation score. The term “real-time” means instantaneously ornear instantaneously (e.g., within milliseconds) and “near real-time”means within a few hours. In some cases, an item selected by theconsumer to purchase may affect one or more facets.

The reputation shopper application can also can recommend a substituteor alternative item that may be more favorable to their digitalreputation score. The information about alternative item may be providedwith a hyperlink. A substitute or alternative item may be more favorableto a user's financial digital reputation score if it is sold at a lowerprice by another vendor or implies favorable behavior (e.g.,responsibility) based on factors such as machine learning findings. Forexample, as shown in FIG. 2B, if a user adds ice cream to his or herlist of items to purchase, the reputation shopper application on aportable electronic device can communicate with a server to determine asame or similar ice cream, for example, ice cream bars, that is pricedlower than the ice cream selected by the user for purchase. The portableelectronic device can receive hyperlinked information about thealternative item, if any, from the server and then display thealternative item.

In some implementations, a server can determine whether to recommend asubstitute or alternative item for purchase by determining a seconddigital reputation score based on the alternative item for purchase. Thesecond digital reputation score can be based on one or more financialfactors as discussed in this document and the alternative item forpurchase. As an example, if the server determines that the seconddigital reputation score is greater or higher than the first digitalreputation score, the server can recommend the alternative item to bedisplayed by the reputation shopper application. In some embodiments,the digital reputation score associated with the alternative item canalso be displayed to the user using, for example, a portable electronicdevice. In another example, if the user purchases furniture protectors,the reputation shopper can use past correlations derived from behaviorpatterns obtained via mechanisms like machine learning to determine thatthe user's reputation score would be improved if they, say, also boughtanother item such as sandpaper because people who purchase those twoitems together are known to be more responsible home owners and have alower financial risk to banks and other potential lenders. The toolwould then make the recommendation to purchase the complimentary item.

In some implementations, the reputation shopper application may alsoprovide information about complementary purchases that may be combinedwith the item that the consumer wants to purchase to enhance theconsumer's digital reputation score. The information about complementarypurchases may be provided with a hyperlink. Continuing with the examplediscussed above, the reputation shopper application on a personalelectronic device can receive from a server and display hyperlinkedinformation about a recommended complementary ice cream cones that canbe used with the ice cream.

In some embodiments, the reputation shopper application may provide tothe user one or more coupons to use with the item that the consumerwants to purchase. In some implementations, the coupons may be providedby a server to a portable electronic device. In some other embodiments,the coupons may also be provided for the alternative or complementaryitems. Further, the reputation shopper application may also be able todetermine whether a user's can delay his or her purchase to get a get abetter deal using, for example, the “buy at another time” feature. Forexample, if a user wants to purchase a television in October and hasadded the television to the reputation shopper application, the servermay determine that televisions tend to be on sale at the end of Novemberand may send this information to the portable electronic device todisplay to the user that the user may want to delay the purchase untilthe end of November. Continuing with this example, in someimplementations, a server may determine that an alternative time isavailable to purchase an item if the alternative time results in ahigher digital reputation score.

In some embodiments, the reputation shopper application may also provideto the user an alternative location to purchase the item selected to bepurchased by the user. As an example, a server can determine a firstlocation of the user based on information gathered from the user'sportable electronic device. The server can determine an alternativelocation to purchaser the item based on the server determining that theuser's purchase of the item at the alternative location results in ahigher digital reputation score than if the user purchased the item atthe first location (e.g., purchasing a product at a local store canresult in a higher digital reputation score than at Wal-Mart).

In some implementations, data regarding the user's digital datareputation score and how the user wants to be viewed can be used by anorganization to generate more targeted advertising. That is, byreviewing the user's digital data reputation score and how the userwould like to be viewed, a server can determine that the user should notbe offered products that the user traditionally would have been offeredand/or offered products that the user traditionally would have beenoffered.

In some embodiments, the digital data reputation information of apopulation and of the user can be used to help the user with otherdecisions such as recommending certain places to live such asneighborhoods, towns, cities, or states that fit the user's digitalreputation or what the user wants for their digital reputation. Inanother example, an analysis of data can suggest where a particularbusiness may thrive based on the population and other businesses thatare in the area or missing from an area. That is, users can be matchedto communities, businesses, friends, entertainment, or other facets oflife based on the digital data reputation of others.

Reputation Data Mapper Application

FIGS. 3A-3B show embodiments of a reputation data mapper applicationthat can be implemented on a web browser or a mobile application. Thereputation data mapper application can analyze the reputation datasources and can provide a view of what user data is accessible, by whom,how it is likely currently being used, how it might be used in thefuture and what the impact of that data is or could be on the digitalreputation score. In some implementations, reputation data mapperapplication includes a source column that can identify the source of thepersonal data leak. As an example, the source may include a name of amobile application for which the user forgot to turn of sharing. In someimplementations, the reputation data mapper application may analyzeinformation similar to the information provided to the digital datareputation platform. Such information may include home or officeaddress, credit card information and purchases, creditor reportinformation, online shopping data, social media data, data fromretailers, blog or media or publications, or other web data.

An example of various pieces of data is provided in FIGS. 3A-3B. In FIG.3A, the reputation data mapper application indicates information that ispublicly viewable such as a person's birthday that was obtained from theperson's social media account. The current use column indicates whatentity or entities use that information. For example, a person'sbirthday may be used by social media for marketing purposes. The protectcolumn may allow the consumer to protect the data. Continuing with theexample discussed above, if the person wants to remove his or herbirthday information from the social media website, the reputation datamapper application can provide information about how to do so. Theconsumer column can provide information about potential additionalthird-parties that may use the data. Finally, the impact column providesinformation about the effect of the publicly viewable data on the user'sreputation.

For instance, the impact column can provide a button which when clickedbrings up a visual pop-up of the reports shown in FIGS. 1A and 1B. Thisway the reputation-mapper tool can provide a simulated view of thefuture impact on your scores for each of the line-items. For instance,the user can uncheck parameters, such as their LinkedIn profile, frombeing shared publicly and review the impact made on their professionalreputation and their social network. Removing their LinkedIn profile mayhave a much larger impact on the user's professional reputation than theuser's social network.

Furthermore, a clickable map icon can be provided in the impact columnas well, which can bring up a US or a global map with potentiallydifferent reputation scores in different regions based on the same dataset. For instance, Europe has stricter privacy controls than the US, andhiding/suppressing Personally Identifiable Information (“PII”) such associal security number may not have a major impact on financialreputation scores in Europe, but could have a major impact in the USwhere a number of Know-Your-Customer (KYC) algorithms run by the majorfinancial institutions rely on such data. In some embodiments, thesystem can generate and display histograms or other visuals that canchange in real-time (e.g., rising, falling) as the user adjustsparameters back and forth.

In FIG. 3B, the reputation data mapper application provides informationthat is selectively accessible using, for example, a passkey orpassword. The data that is selectively accessible may include, forexample, credit card information stored in the user's onlinesubscription services. In FIG. 3B, the columns used to describeselective access may be the same as the ones used for publiclyaccessible information, as shown in FIG. 3A.

Reputation Data Masker Application

A reputation data masker application can provide the consumer with amechanism to mask their payment data when making in person, online ordigital purchases to prevent purchase information from being mapped backto the individual making the purchase from the credit card issuer.Purchase information can include the specific items or servicespurchased, where it was purchased, time it was purchased, among otherthings. In some implementations, the reputation data masker applicationmay receive a signal from a credit card that informs the applicationthat the user prefers to mask his or her information. As an example, acredit card (e.g., plastic credit card with a chip or virtual card inmobile wallet) may include a switch or a select option that a user canuse to turn on or off the protect feature that allows the user to maskor unmask partial or all transaction information. When the reputationdata masker application receives the signal from the credit card thatthe user has turned on the protect feature, the application can masksome or all of the information associated with the credit cardtransaction. In some embodiments, the credit card with the switch is aghost card that is linked to the user's actual credit card. In this way,the transaction is charged to the ghost card and the system charges theuser's actual credit card but provides only the information indicated bythe switch or the application. The credit card related implementationcan be applied to other cards, such as rewards cards, or mobile payservices. In some embodiments, the point of sale can ask the user thelevel of information the user would like to have shared.

Today the payment rails (VISA, MasterCard, etc.) and billing systemsalready perform a level of masking, e.g., the last 4 digits of creditcard information is shown on public facing information. In thereputation data masker application, the last four digits could beintegrated into the edge such as point of sale services, ATMs, or theycould be part of the transaction flow in the backend systems where thereputation mask component is interjected for data outbound from thesesystems. User-configurable rules can be applied to any data shared to apublic reputation system or dashboard and the data sent is maskedaccordingly. Data masking in some embodiments can also be a real-timeinteraction with the user at the time of publication, similar toreceiving a text message on the user's phone, e.g.: “Your amazon TVpurchase rating is being shared, text YES/NO to share your Full Name, ifnot Only your First name and Last initial will be shared.”

Reputation services can be deployed where retailers, banks and otherinstitutions can leverage and all the masking can occur behind theseservices (e.g., the cloud model). In a decentralized model such asblockchain, the reputation can be more tightly controlled by a user withgreater clarity allowing more sophisticated interactions.

A benefit of masking the user's personal information is that it canprevent the purchased item's disclosure to a third-party, which mayimpact the user's digital reputation score. By masking personalinformation, the user may prevent breaches of privacy. As an example,when a user purchases a baby crib, the user may mask that information toprevent a store or a credit card company from knowing that someone inthe user's household is pregnant. Another benefit of the reputation datamasker application is that the users can choose to only allow thedissemination of certain information that help their reputation andexclude those that would hurt. For example, a health conscious user mayallow the purchase of a gym membership to impact his or her digitalreputation score, but may not want the purchase of a fast-food meal toimpact his personal reputation.

FIG. 4 is a block diagram illustrating an overview of devices on whichsome implementations of the disclosed technology can operate. Thedevices can comprise hardware components of a device 400 that managesthe digital data reputation platform 464 that may include informationassociated with any one or more of a digital data reputation scorereport, a reputation shopper application, a reputation data mapperapplication, and a reputation data masker application. Device 400 caninclude one or more input devices 420 that provide input to the CPU(processor) 410, notifying it of actions. The actions are typicallymediated by a hardware controller that interprets the signals receivedfrom the input device and communicates the information to the CPU 410using a communication protocol. Input devices 420 include, for example,a mouse, a keyboard, a touchscreen, an infrared sensor, a touchpad, awearable input device, a camera- or image-based input device, amicrophone, or other user input devices.

CPU 410 can be a single processing unit or multiple processing units ina device or distributed across multiple devices. CPU 410 can be coupledto other hardware devices, for example, with the use of a bus, such as aPCI bus or SCSI bus. The CPU 410 can communicate with a hardwarecontroller for devices, such as for a display 430. Display 430 can beused to display text and graphics. In some examples, display 430provides graphical and textual visual feedback to a user. In someimplementations, display 430 includes the input device as part of thedisplay, such as when the input device is a touchscreen or is equippedwith an eye direction monitoring system. In some implementations, thedisplay is separate from the input device. Examples of display devicesare: an LCD display screen; an LED display screen; a projected,holographic, or augmented reality display (such as a heads-up displaydevice or a head-mounted device); and so on. Other I/O devices 440 canalso be coupled to the processor, such as a network card, video card,audio card, USB, FireWire or other external device, camera, printer,speakers, CD-ROM drive, DVD drive, disk drive, or Blu-Ray device.

In some implementations, the device 400 also includes a communicationdevice capable of communicating wirelessly or wire-based with a networknode. The communication device can communicate with another device or aserver through a network using, for example, TCP/IP protocols. Device400 can utilize the communication device to distribute operations acrossmultiple network devices.

The CPU 410 can have access to a memory 450. A memory includes one ormore of various hardware devices for volatile and non-volatile storage,and can include both read-only and writable memory. For example, amemory can comprise random access memory (RAM), CPU registers, read-onlymemory (ROM), and writable non-volatile memory, such as flash memory,hard drives, floppy disks, CDs, DVDs, magnetic storage devices, tapedrives, device buffers, and so forth. A memory is not a propagatingsignal divorced from underlying hardware; a memory is thusnon-transitory. Memory 450 can include program memory 460 that storesprograms and software, such as an operating system 462, digital datareputation platform 464, and other application programs 466. Memory 450can also include data memory 470 that can include digital reputationscores for personal, professional, or financial aspects of a user,suggestions for the user to do or to continue doing an activity,suggestions for to user to stop doing an activity, effect of the user'splanned purchase on the user's digital reputation score, suggestions foralternative or additional purchases, information about relevant coupons,recommendations for the user to delay his or her planned purchases,information about and control for personal information that is publiclyviewable or is selectively accessed, etc., which can be provided to theprogram memory 460 or any element of the device 400.

Some implementations can be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with the technologyinclude, but are not limited to, personal computers, server computers,handheld or laptop devices, cellular telephones, portable electronicdevices such as smartphones, wearable electronics, gaming consoles,tablet devices, multiprocessor systems, microprocessor-based systems,set-top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, distributed computing environmentsthat include any of the above systems or devices, or the like.

FIG. 5 is a block diagram illustrating an overview of an environment 500in which some implementations of the disclosed technology can operate.Environment 500 can include one or more client computing devices 505A-D,examples of which can include device 400. Client computing devices505A-D can operate in a networked environment using logical connectionsthrough network 530 to one or more remote computers, such as a servercomputing device 510. As shown in FIG. 5, examples of client computingdevices 505A-D may include a portable electronic device 505A, a computer505B, a server 505C, or a laptop 505D.

In some implementations, server computing device 510 can be an edgeserver that receives client requests and coordinates fulfillment ofthose requests through other servers, such as servers 520A-C. Servercomputing devices 510 and 520A-C can comprise computing systems, such asdevice 400. Though each server computing device 510 and 520A-C isdisplayed logically as a single server, server computing devices caneach be a distributed computing environment encompassing multiplecomputing devices located at the same or at geographically disparatephysical locations. In some implementations, each server computingdevice 520 corresponds to a group of servers.

Client computing devices 505A-D and server computing devices 510 and520A-C can each act as a server or client to other server/clientdevices. Server 510 can connect to a database 515. Servers 520A-C caneach connect to a corresponding database 525A-C. As discussed above,each server 520 can correspond to a group of servers, and each of theseservers can share a database or can have their own database. Databases515 and 525 can warehouse (e.g., store) information such as digitalreputation scores for personal, professional, or financial aspects of auser, suggestions for the user to do or to continue doing an activity,suggestions for to user to stop doing an activity, effect of the user'splanned purchase on the user's digital reputation score, suggestions foralternative or additional purchases, information about relevant coupons,recommendations for the user to delay his or her planned purchases,information about and control for personal information that is publiclyviewable or is selectively accessed. Though databases 515 and 525 aredisplayed logically as single units, databases 515 and 525 can each be adistributed computing environment encompassing multiple computingdevices, can be located within their corresponding server, or can belocated at the same or at geographically disparate physical locations.

Network 530 can be a local area network (LAN) or a wide area network(WAN), but can also be other wired or wireless networks. Network 530 maybe the Internet or some other public or private network. Clientcomputing devices 505 can be connected to network 530 through a networkinterface, such as by wired or wireless communication. While theconnections between server 510 and servers 520A-C are shown as separateconnections, these connections can be any kind of local, wide area,wired, or wireless network, including network 530 or a separate publicor private network.

FIG. 6 is a block diagram illustrating components 600 which, in someimplementations, can be used in a system employing the disclosedtechnology. The components 600 include hardware 602, general software620, and specialized components 640. As discussed above, a systemimplementing the disclosed technology can use various hardware,including processing units 604 (e.g., CPUs, GPUs, APUs, etc.), workingmemory 606, storage memory 608, and input and output devices 610. Someor all of the components 600 can be implemented in a client computingdevice such as client computing devices 505A-D or on a server computingdevice, such as server computing device 510 or 520A-C. For example, adigital data reputation platform system may include a mobile applicationrunning on a mobile device and a remote server. The mobile applicationcan be configured to collect information identifying an item forpurchase, and send the information identifying the item for purchase toa remote server. The remote server can be configured to receive theinformation identifying the item for purchase, and generate a digitalreputation score for a user, wherein the digital reputation score isbased at least in part on the item. The digital reputation score can begenerated by the remote server configured to scan internet websites forreputational information related to the user, analyze the reputationalinformation based at least in part on a set of keywords, and score thereputational information based at least in part on a set of keywordsspecific to each website. The mobile application can be configured toreceive the digital reputation score, and display, on a mobile devicerunning the mobile application, the digital reputation score andindicate an impact of the item on the digital reputation score.

General software 620 can include various applications, including anoperating system 622, local programs 624, and a basic input outputsystem (BIOS) 626. Specialized components 640 can be subcomponents of ageneral software application 620, such as local programs 624.Specialized components 640 can include any one or more of digital datareputation module 644, reputation shopper module 646, reputation datamapper module 648, and reputation data masker module 650, and componentsthat can be used for transferring data and controlling the specializedcomponents, such as interface 642. In some implementations, components600 can be in a computing system that is distributed across multiplecomputing devices or can be an interface to a server-based applicationexecuting one or more of specialized components 640.

The digital data reputation module 644 can perform the featuresassociated with the digital data reputation score report as disclosed inthis document. For example, digital data reputation module 644 can allowa user to select one or more facets to determine the user's digitalreputation score for the selected one or more facets. As an example,digital data reputation module 644 can allow a user to select personal,professional, and financial facets. Based on such selection, the digitaldata reputation module 644 can determine the user's reputation for eachfacet. In some embodiments, the digital data reputation module 644 maydetermine the digital reputation score for each facet based on any oneor more of sources available on the internet, through retailers,solicitors, social media, publications, self-published content, creditcard information, online shopping data, web history, or any otherdigital data store that can be obtained either through digital mining orthrough the user's consent.

In some implementations, the digital data reputation module 644 can alsodetermine a list of one or more activities or behaviors that are helpingor hurting the user's digital reputation score for the one or morefacets. Further, the digital data reputation module 644 can alsorecommend an additional list of one or more activities that may improvethe user's digital reputation score for the one or more facets.

In some embodiments the digital data reputation module 644 can determinereputation sub-scores for the one or more facets selected by the user.For example, as shown in FIG. 1B, the digital data reputation module 644can determine reputation sub-scores for the personal facet for family,friends, community, lifestyle, faith, and health.

The reputation shopper module 646 can perform the features associatedwith the reputation shopper application as disclosed in this document.For example, the reputation shopper module 646 can allow the user toscan or take a picture of an item that the user wants to purchase. Whenan item is added to be purchased, the reputation shopper module 646 candetermine the effect of the item's purchase on the user's reputation. Insome implementations, the reputation shopper module 646 can use thedigital data reputation module 644 to determine the effect of the itemto be purchased on the user's reputation. The reputation shopper module646 can also recommend substitutes or alternatives for the user topurchase instead of the item that the user added to be purchased. Insome implementations, the reputation shopper module 646 can recommendcomplementary items to purchase in addition to the item that the userwants to purchase. The reputation shopper module 646 can also recommendcoupons for the item the user wants to purchase or determine a bettertime to purchase the item.

The reputation data mapper module 648 can perform the featuresassociated with the reputation data mapper application as disclosed inthis document. For example, the reputation data mapper module 648 canprovide the user with a list of one or more personal information that ispublicly viewable or is selectively accessible. For each personalinformation, the reputation data mapper module 648 can determine thescore and current use. Further, the reputation data mapper module 648can also recommend ways to protect that information. In someimplementations, the reputation data mapper module 648 can provideinformation about the third-parties that may be using the user'spersonal information. Further, the reputation data mapper module 648 canalso use the digital data reputation module 644 to characterize theimpact of the publicly viewable or selectively accessible personalinformation on the user's digital reputation score.

The reputation data masker module 650 can perform the featuresassociated with the reputation data masker application as disclosed inthis document.

Those skilled in the art will appreciate that the components illustratedin FIGS. 4-6 described above, and in each of the flow diagrams discussedbelow, may be altered in a variety of ways. For example, the order ofthe logic may be rearranged, substeps may be performed in parallel,illustrated logic may be omitted, other logic may be included, etc. Insome implementations, one or more of the components described above canexecute one or more of the processes described below.

FIG. 7 is a flow diagram illustrating a set of operations 700 formanaging a digital reputation of a user. In some embodiments, in thisand other flow diagrams of operations, fewer than all of the operationsin the set of operations are performed, whereas, in other embodiments,additional operations are performed. Moreover, in some embodiments, theoperations may be performed in different orders or in parallel. Theoperations can be performed by components illustrated in FIGS. 4-7.

At the receiving operation 702, information is received about an item tobe purchased by the user. In some embodiments, the received informationcan be collected via a portable electronic device. At the determiningoperation, a server can determine in real-time or near real-time, basedon one or more financial factors, a first digital reputation scoreindicative of an effect of the item on the user's digital reputation.The first digital reputation score can be displayed to the user via theportable electronic device. At the second determining operation 706, theserver can determine an alternative item to be purchased instead of theitem to be purchased. The alternative item can be displayed to the uservia the portable electronic device. At the fourth determining operation708, the server can determine a second digital reputation score based onthe purchase of the alternative item. The second digital reputationscore can be displayed to the user via the portable electronic device.

CONCLUSION

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof means any connection or coupling,either direct or indirect, between two or more elements; the coupling orconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this application, refer tothis application as a whole and not to any particular portions of thisapplication. Where the context permits, words in the above DetailedDescription using the singular or plural number may also include theplural or singular number respectively. The word “or,” in reference to alist of two or more items, covers all of the following interpretationsof the word: any of the items in the list, all of the items in the list,and any combination of the items in the list. The description and claimsuse “digital data reputation” and “digital reputation” interchangeably.The description and claims also use “digital data reputation score” and“digital reputation score” interchangeably.

Several implementations of the disclosed technology are described abovein reference to the figures. The computing devices on which thedescribed technology may be implemented can include one or more centralprocessing units, memory, input devices (e.g., keyboards and pointingdevices), output devices (e.g., display devices), storage devices (e.g.,disk drives), and network devices (e.g., network interfaces). The memoryand storage devices are computer-readable storage media that can storeinstructions that implement at least portions of the describedtechnology. In addition, the data structures and message structures canbe stored or transmitted via a data transmission medium, such as asignal on a communications link. Various communications links can beused, such as the Internet, a local area network, a wide area network,or a point-to-point dial-up connection. Thus, computer-readable mediacan comprise computer-readable storage media (e.g., “non-transitory”media) and computer-readable transmission media.

As used herein, being above a threshold means that a value for an itemunder comparison is above a specified other value, that an item undercomparison is among a certain specified number of items with the largestvalue, or that an item under comparison has a value within a specifiedtop percentage value. As used herein, being below a threshold means thata value for an item under comparison is below a specified other value,that an item under comparison is among a certain specified number ofitems with the smallest value, or that an item under comparison has avalue within a specified bottom percentage value. As used herein, beingwithin a threshold means that a value for an item under comparison isbetween two specified other values, that an item under comparison isamong a middle specified number of items, or that an item undercomparison has a value within a middle specified percentage range.

As used herein, the word “or” refers to any possible permutation of aset of items. For example, the phrase “A, B, or C” refers to at leastone of A, B, C, or any combination thereof, such as any of: A; B; C; Aand B; A and C; B and C; A, B, and C; or multiple of any item, such as Aand A; B, B, and C; A, A, B, C, and C; etc.

The above Detailed Description of examples of the technology is notintended to be exhaustive or to limit the technology to the precise formdisclosed above. While specific examples for the technology aredescribed above for illustrative purposes, various equivalentmodifications are possible within the scope of the technology. Forexample, while processes or blocks are presented in a given order,alternative implementations may perform routines having steps, or employsystems having blocks, in a different order, and some processes orblocks may be deleted, moved, added, subdivided, combined, and/ormodified to provide alternative or subcombinations. Each of theseprocesses or blocks may be implemented in a variety of different ways.Also, while processes or blocks are at times shown as being performed inseries, these processes or blocks may instead be performed orimplemented in parallel, or may be performed at different times. Furtherany specific numbers noted herein are only examples: alternativeimplementations may employ differing values or ranges.

The teachings of the technology provided herein can be applied to othersystems, not necessarily the system described above. The elements andacts of the various examples described above can be combined to providefurther implementations of the technology. Some alternativeimplementations of the technology may include not only additionalelements to those implementations noted above, but also may includefewer elements.

These and other changes can be made to the technology in light of theabove Detailed Description. While the above description describescertain examples of the technology, and describes the best modecontemplated, no matter how detailed the above appears in text, thetechnology can be practiced in many ways. Details of the system may varyconsiderably in its specific implementation, while still beingencompassed by the technology disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the technology should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the technology with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the technology to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe technology encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the technology under theclaims.

To reduce the number of claims, certain aspects of the technology arepresented below in certain claim forms, but the applicant contemplatesthe various aspects of the technology in any number of claim forms. Forexample, while only one aspect of the technology is recited as acomputer-readable medium claim, other aspects may likewise be embodiedas a computer-readable medium claim, or in other forms, such as beingembodied in a means-plus-function claim. Any claims intended to betreated under 35 U.S.C. § 112(f) will begin with the words “means for”,but use of the term “for” in any other context is not intended to invoketreatment under 35 U.S.C. § 112(f). Accordingly, the applicant reservesthe right to pursue additional claims after filing this application topursue such additional claim forms, in either this application or in acontinuing application.

We claim:
 1. A method of managing a digital reputation of a user bysuggesting alternative purchases, the method comprising: receiving, viaa portable electronic device, information about an item to be purchasedby a user wherein the information about the item is received by at leastone of: scanning a bar code on the item, obtaining a photo of the item,or receiving contents of a shopping cart including the item; determiningin real-time or near real-time, based on one or more financial factors,a first digital reputation score indicative of an effect of the item ona digital reputation of the user; causing the first digital reputationscore to be displayed to the user via the portable electronic device;determining an alternative item to be purchased instead of the item tobe purchased causing an indication of the alternative item to bedisplayed to the user via the portable electronic device; determining asecond digital reputation score based on the purchase of the alternativeitem; and causing the second digital reputation score to be is-displayedto the user via the portable electronic device.
 2. The method of claim1, wherein the first and second digital reputation scores are displayedas markers on a line or bar that includes colors characterizing thefirst and second digital reputation scores.
 3. The method of claim 2,wherein a green color characterizes an excellent digital reputationscore, a yellow color characterizes a moderate digital reputation, and ared color characterizes a poor digital reputation score.
 4. The methodof claim 1, wherein the one or more financial factors for the userinclude spending habit, saving habit, age, income, shoppingsubscriptions, social media postings, bankruptcies, or any combinationthereof.
 5. The method of claim 1, wherein the information about theitem is received by at least one of: scanning a bar code on the item orobtaining a photo of a shopping cart including the item.
 6. The methodof claim 1, wherein the alternative item is displayed with a hyperlink.7. The method of claim 1, further comprising: determining one or morecoupons to be used for the item to be purchased, wherein the one or morecoupons are displayed to the user.
 8. The method of claim 1, furthercomprising: determining an alternative time or date to purchase theitem, wherein the alternative time or date results in a higher digitalreputation score, and wherein the alternative time or date is displayedto the user.
 9. The method of claim 1, further comprising: masking thereceived information about the item from an identity of the user toprevent purchase information from being mapped back to the user, themasking of the received information being based on a user preferenceinformation received from a payment card.
 10. The method of claim 1,further comprising: determining a location of the user via informationgathered from the portable electronic device; and determining analternative location to purchase the item, where purchasing the item inthe alternative location results in a higher digital reputation score ofthe user than purchasing the item in the location.
 11. The method ofclaim 1, further comprising: determining an additional item to bepurchased with the item to be purchased, wherein the additional item isdisplayed to the user.
 12. A non-transitory computer-readable storagemedium storing instructions that, when executed by a computing system,cause the computing system to perform a process comprising: receivinginformation about an item to be purchased by the user, wherein theinformation is collected via a portable electronic device; determiningin real-time or near real-time, based on one or more financial factors,a first digital reputation score indicative of an effect of the item onthe user's digital reputation; causing the first digital reputationscore to be displayed to the user via the portable electronic device;determining an alternative item to be purchased instead of the item tobe purchased; causing an indication of the alternative item to bedisplayed to the user via the portable electronic device; determine asecond digital reputation score based on the purchase of the alternativeitem; and causing the second digital reputation score to be displayed tothe user via the portable electronic device.
 13. The non-transitorycomputer-readable storage medium of claim 12, wherein the first andsecond digital reputation scores are displayed as markers on a line orbar that includes colors characterizing the first and second digitalreputation scores.
 14. The non-transitory computer-readable storagemedium of claim 13, wherein a green color characterizes an excellentdigital reputation score, a yellow color characterizes a moderatedigital reputation, and a red color characterizes a poor digitalreputation score.
 15. The non-transitory computer-readable storagemedium of claim 12, wherein the one or more financial factors for theuser include spending habit, saving habit, age, income, shoppingsubscriptions, social media postings, bankruptcies, or publiclyavailable information.
 16. The non-transitory computer-readable storagemedium of claim 12, wherein the information about the item is receivedby at least one of: scanning a bar code on the item, obtaining a photoof the item, or receiving contents of a shopping cart including theitem.
 17. A computing system comprising: one or more processors; and oneor more memories storing instructions that, when executed by a computingsystem, cause the computing system to perform a process comprising:receiving information about an item to be purchased by the user, whereinthe information is collected via a portable electronic device;determining in real-time or near real-time, based on one or morefinancial factors, a first digital reputation score indicative of aneffect of the item on the user's digital reputation; wherein the firstdigital reputation score is displayed to the user via the portableelectronic device; determining an alternative item to be purchasedinstead of the item to be purchased; wherein an indication of thealternative item is displayed to the user via the portable electronicdevice; and determining a second digital reputation score based on thepurchase of the alternative item; wherein the second digital reputationscore is displayed to the user via the portable electronic device. 18.The computing system of claim 17, wherein the process further comprises:determining an alternative time or date to purchase the item, whereinthe alternative time or date results in a higher digital reputationscore, and wherein the alternative time or date is displayed to theuser.
 19. The computing system of claim 17, wherein the process furthercomprises: masking the received information about the item from anidentity of the user to prevent purchase information from being mappedback to the user, the masking of the received information being based ona user preference information received from a payment card.
 20. Thecomputing system of claim 17, wherein the process further comprises:determining a location of the user via information gathered from theportable electronic device; and determining an alternative location topurchase the item, where purchasing the item in the alternative locationresults in a higher digital reputation score of the user than purchasingthe item in the location.